class_docstr: Apply Gaussian Blur to the PIL image. Take the radius and probability of
  application as the parameter.
  
  This transform was used in SimCLR - https://arxiv.org/abs/2002.05709
docstr: Args:
      p (float): probability of applying gaussian blur to the image
      radius_min (float): blur kernel minimum radius used by ImageFilter.GaussianBlur
      radius_max (float): blur kernel maximum radius used by ImageFilter.GaussianBlur
docstr_tok: ["Args",":","p","(","float",")",":","probability","of","applying","gaussian","blur","to","the","image","radius_min","(","float",")",":","blur","kernel","minimum","radius","used","by","ImageFilter.GaussianBlur","radius_max","(","float",")",":","blur","kernel","maximum","radius","used","by","ImageFilter.GaussianBlur"]
code: def __init__(self, p, radius_min, radius_max):
      """
      Args:
          p (float): probability of applying gaussian blur to the image
          radius_min (float): blur kernel minimum radius used by ImageFilter.GaussianBlur
          radius_max (float): blur kernel maximum radius used by ImageFilter.GaussianBlur
      """
      self.prob = p
      self.radius_min = radius_min
      self.radius_max = radius_max
  
file_path: vissl/vissl/data/ssl_transforms/img_pil_gaussian_blur.py
imports: import random
  from typing import Any, Dict
  import numpy
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from PIL import ImageFilter
label_desc: Use image node_id 1 to create a new ImgPilGaussianBlur object from inherited base classes: ClassyTransform with example: obj = ImgPilGaussianBlur(p, radius_min, radius_max)